






"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = AudioLDMPipeline
    __UpperCAmelCase           = TEXT_TO_AUDIO_PARAMS
    __UpperCAmelCase           = TEXT_TO_AUDIO_BATCH_PARAMS
    __UpperCAmelCase           = frozenset(
        [
            """num_inference_steps""",
            """num_waveforms_per_prompt""",
            """generator""",
            """latents""",
            """output_type""",
            """return_dict""",
            """callback""",
            """callback_steps""",
        ])







    def 				a__			(		self :	int ):





           '''simple docstring'''

           torch.manual_seed(0 )
           __magic_name__      				=    UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)				,    layers_per_block=2				,    sample_size=3_2				,    in_channels=4				,    out_channels=4				,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,    cross_attention_dim=(3_2, 6_4)				,    class_embed_type='simple_projection'				,    projection_class_embeddings_input_dim=3_2				,    class_embeddings_concat=UpperCamelCase_				,    )
           __magic_name__      				=    DDIMScheduler(
               beta_start=0.00_085				,    beta_end=0.012				,    beta_schedule='scaled_linear'				,    clip_sample=UpperCamelCase_				,    set_alpha_to_one=UpperCamelCase_				,    )
           torch.manual_seed(0 )
           __magic_name__      				=    AutoencoderKL(
               block_out_channels=[3_2, 6_4]				,    in_channels=1				,    out_channels=1				,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,    latent_channels=4				,    )
           torch.manual_seed(0 )
           __magic_name__      				=    ClapTextConfig(
               bos_token_id=0				,    eos_token_id=2				,    hidden_size=3_2				,    intermediate_size=3_7				,    layer_norm_eps=1e-0_5				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1_0_0_0				,    projection_dim=3_2				,    )
           __magic_name__      				=    ClapTextModelWithProjection(UpperCamelCase_ )
           __magic_name__      				=    RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'				,    model_max_length=7_7 )

           __magic_name__      				=    SpeechTaHifiGanConfig(
               model_in_dim=8				,    sampling_rate=1_6_0_0_0				,    upsample_initial_channel=1_6				,    upsample_rates=[2, 2]				,    upsample_kernel_sizes=[4, 4]				,    resblock_kernel_sizes=[3, 7]				,    resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]				,    normalize_before=UpperCamelCase_				,    )

           __magic_name__      				=    SpeechTaHifiGan(UpperCamelCase_ )

           __magic_name__      				=    {
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'vocoder': vocoder,
           }
           return components







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Optional[int]=0 ):





           '''simple docstring'''

           if str(UpperCamelCase_ ).startswith('mps' ):
                  __magic_name__      				=    torch.manual_seed(UpperCamelCase_ )
           else:
                  __magic_name__      				=    torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           __magic_name__      				=    {
               'prompt': 'A hammer hitting a wooden surface',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
           }
           return inputs







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    'cpu'  # ensure determinism for the device-dependent torch.Generator

           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           assert audio.ndim == 1
           assert len(UpperCamelCase_ ) == 2_5_6

           __magic_name__      				=    audio[:1_0]
           __magic_name__      				=    np.array(
               [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )

           assert np.abs(audio_slice - expected_slice ).max() < 1e-2







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    3 * [inputs['prompt']]

           # forward
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    3 * [inputs.pop('prompt' )]

           __magic_name__      				=    audioldm_pipe.tokenizer(
               UpperCamelCase_				,    padding='max_length'				,    max_length=audioldm_pipe.tokenizer.model_max_length				,    truncation=UpperCamelCase_				,    return_tensors='pt'				,    )
           __magic_name__      				=    text_inputs['input_ids'].to(UpperCamelCase_ )

           __magic_name__      				=    audioldm_pipe.text_encoder(
               UpperCamelCase_				,    )
           __magic_name__      				=    prompt_embeds.text_embeds
           # additional L_2 normalization over each hidden-state
           __magic_name__      				=    F.normalize(UpperCamelCase_				,    dim=-1 )

           __magic_name__      				=    prompt_embeds

           # forward
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           assert np.abs(audio_a - audio_a ).max() < 1e-2







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    3 * ['this is a negative prompt']
           __magic_name__      				=    negative_prompt
           __magic_name__      				=    3 * [inputs['prompt']]

           # forward
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    3 * [inputs.pop('prompt' )]

           __magic_name__      				=    []
           for p in [prompt, negative_prompt]:
                  __magic_name__      				=    audioldm_pipe.tokenizer(
                      UpperCamelCase_				,    padding='max_length'				,    max_length=audioldm_pipe.tokenizer.model_max_length				,    truncation=UpperCamelCase_				,    return_tensors='pt'				,    )
                  __magic_name__      				=    text_inputs['input_ids'].to(UpperCamelCase_ )

                  __magic_name__      				=    audioldm_pipe.text_encoder(
                      UpperCamelCase_				,    )
                  __magic_name__      				=    text_embeds.text_embeds
                  # additional L_2 normalization over each hidden-state
                  __magic_name__      				=    F.normalize(UpperCamelCase_				,    dim=-1 )

                  embeds.append(UpperCamelCase_ )

           __magic_name__				,  __magic_name__      				=    embeds

           # forward
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           assert np.abs(audio_a - audio_a ).max() < 1e-2







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    'cpu'  # ensure determinism for the device-dependent torch.Generator
           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    'egg cracking'
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_				,    negative_prompt=UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           assert audio.ndim == 1
           assert len(UpperCamelCase_ ) == 2_5_6

           __magic_name__      				=    audio[:1_0]
           __magic_name__      				=    np.array(
               [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )

           assert np.abs(audio_slice - expected_slice ).max() < 1e-2







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    'cpu'  # ensure determinism for the device-dependent torch.Generator
           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    'A hammer hitting a wooden surface'

           # test num_waveforms_per_prompt=1 (default)
           __magic_name__      				=    audioldm_pipe(UpperCamelCase_				,    num_inference_steps=2 ).audios

           assert audios.shape == (1, 2_5_6)

           # test num_waveforms_per_prompt=1 (default) for batch of prompts
           __magic_name__      				=    2
           __magic_name__      				=    audioldm_pipe([prompt] * batch_size				,    num_inference_steps=2 ).audios

           assert audios.shape == (batch_size, 2_5_6)

           # test num_waveforms_per_prompt for single prompt
           __magic_name__      				=    2
           __magic_name__      				=    audioldm_pipe(UpperCamelCase_				,    num_inference_steps=2				,    num_waveforms_per_prompt=UpperCamelCase_ ).audios

           assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

           # test num_waveforms_per_prompt for batch of prompts
           __magic_name__      				=    2
           __magic_name__      				=    audioldm_pipe(
               [prompt] * batch_size				,    num_inference_steps=2				,    num_waveforms_per_prompt=UpperCamelCase_ ).audios

           assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    'cpu'  # ensure determinism for the device-dependent torch.Generator
           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.vocoder.config.sampling_rate

           __magic_name__      				=    self.get_dummy_inputs(UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe(audio_length_in_s=0.016				,    **UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           assert audio.ndim == 1
           assert len(UpperCamelCase_ ) / vocoder_sampling_rate == 0.016

           __magic_name__      				=    audioldm_pipe(audio_length_in_s=0.032				,    **UpperCamelCase_ )
           __magic_name__      				=    output.audios[0]

           assert audio.ndim == 1
           assert len(UpperCamelCase_ ) / vocoder_sampling_rate == 0.032







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    self.get_dummy_components()
           __magic_name__      				=    AudioLDMPipeline(**UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    ['hey']

           __magic_name__      				=    audioldm_pipe(UpperCamelCase_				,    num_inference_steps=1 )
           __magic_name__      				=    output.audios.shape
           assert audio_shape == (1, 2_5_6)

           __magic_name__      				=    audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           __magic_name__      				=    SpeechTaHifiGan(UpperCamelCase_ ).to(UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe(UpperCamelCase_				,    num_inference_steps=1 )
           __magic_name__      				=    output.audios.shape
           # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
           assert audio_shape == (1, 2_5_6)







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=UpperCamelCase_ )







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           self._test_inference_batch_single_identical(test_mean_pixel_difference=UpperCamelCase_ )







    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,    reason='XFormers attention is only available with CUDA and `xformers` installed'				,    )
    def 				a__			(		self :	str ):





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCamelCase_ )



@slow
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def 				a__			(		self :	int				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	str="cpu"				,    UpperCamelCase_ :	Union[str, Any]=torch.floataa				,    UpperCamelCase_ :	Optional[int]=0 ):





           '''simple docstring'''

           __magic_name__      				=    torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           __magic_name__      				=    np.random.RandomState(UpperCamelCase_ ).standard_normal((1, 8, 1_2_8, 1_6) )
           __magic_name__      				=    torch.from_numpy(UpperCamelCase_ ).to(device=UpperCamelCase_				,    dtype=UpperCamelCase_ )
           __magic_name__      				=    {
               'prompt': 'A hammer hitting a wooden surface',
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 2.5,
           }
           return inputs







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    self.get_inputs(UpperCamelCase_ )
           __magic_name__      				=    2_5
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ ).audios[0]

           assert audio.ndim == 1
           assert len(UpperCamelCase_ ) == 8_1_9_2_0

           __magic_name__      				=    audio[7_7_2_3_0:7_7_2_4_0]
           __magic_name__      				=    np.array(
               [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
           __magic_name__      				=    np.abs(expected_slice - audio_slice ).max()
           assert max_diff < 1e-2







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
           __magic_name__      				=    LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
           __magic_name__      				=    audioldm_pipe.to(UpperCamelCase_ )
           audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    self.get_inputs(UpperCamelCase_ )
           __magic_name__      				=    audioldm_pipe(**UpperCamelCase_ ).audios[0]

           assert audio.ndim == 1
           assert len(UpperCamelCase_ ) == 8_1_9_2_0

           __magic_name__      				=    audio[2_7_7_8_0:2_7_7_9_0]
           __magic_name__      				=    np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
           __magic_name__      				=    np.abs(expected_slice - audio_slice ).max()
           assert max_diff < 3e-2







"""simple docstring"""
import argparse
import copy


def 							A						(				__snake_case: Union[str, Any]  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    {}

       with open(__snake_case  ) as f:
              for line in f:
                     if line.split()[0] not in dict_of_neighbours:
                            __magic_name__      				=    []
                            _list.append([line.split()[1], line.split()[2]]  )
                            __magic_name__      				=    _list
                     else:
                            dict_of_neighbours[line.split()[0]].append(
                                [line.split()[1], line.split()[2]]  )
                     if line.split()[1] not in dict_of_neighbours:
                            __magic_name__      				=    []
                            _list.append([line.split()[0], line.split()[2]]  )
                            __magic_name__      				=    _list
                     else:
                            dict_of_neighbours[line.split()[1]].append(
                                [line.split()[0], line.split()[2]]  )

       return dict_of_neighbours


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: str  )    ->		List[str]:

       """simple docstring"""





       with open(__snake_case  ) as f:
              __magic_name__      				=    f.read(1  )
       __magic_name__      				=    start_node

       __magic_name__      				=    []

       __magic_name__      				=    start_node

       __magic_name__      				=    0
       while visiting not in first_solution:
              __magic_name__      				=    1_0_0_0_0
              for k in dict_of_neighbours[visiting]:
                     if int(k[1]  ) < int(__snake_case  ) and k[0] not in first_solution:
                            __magic_name__      				=    k[1]
                            __magic_name__      				=    k[0]

              first_solution.append(__snake_case  )
              __magic_name__      				=    distance_of_first_solution + int(__snake_case  )
              __magic_name__      				=    best_node

       first_solution.append(__snake_case  )

       __magic_name__      				=    0
       for k in dict_of_neighbours[first_solution[-2]]:
              if k[0] == start_node:
                     break
              position += 1

       __magic_name__      				=    (
           distance_of_first_solution
           + int(dict_of_neighbours[first_solution[-2]][position][1]  )
           - 1_0_0_0_0
       )
       return first_solution, distance_of_first_solution


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: Optional[Any]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    []

       for n in solution[1:-1]:
              __magic_name__      				=    solution.index(__snake_case  )
              for kn in solution[1:-1]:
                     __magic_name__      				=    solution.index(__snake_case  )
                     if n == kn:
                            continue

                     __magic_name__      				=    copy.deepcopy(__snake_case  )
                     __magic_name__      				=    kn
                     __magic_name__      				=    n

                     __magic_name__      				=    0

                     for k in _tmp[:-1]:
                            __magic_name__      				=    _tmp[_tmp.index(__snake_case  ) + 1]
                            for i in dict_of_neighbours[k]:
                                   if i[0] == next_node:
                                          __magic_name__      				=    distance + int(i[1]  )
                     _tmp.append(__snake_case  )

                     if _tmp not in neighborhood_of_solution:
                            neighborhood_of_solution.append(_tmp  )

       __magic_name__      				=    len(neighborhood_of_solution[0]  ) - 1

       neighborhood_of_solution.sort(key=lambda __snake_case  : x[index_of_last_item_in_the_list]  )
       return neighborhood_of_solution


def 							A						(				__snake_case: List[str] ,     __snake_case: Union[str, Any] ,     __snake_case: List[str] ,     __snake_case: Optional[int] ,     __snake_case: List[str]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    1
       __magic_name__      				=    first_solution
       __magic_name__      				=    []
       __magic_name__      				=    distance_of_first_solution
       __magic_name__      				=    solution

       while count <= iters:
              __magic_name__      				=    find_neighborhood(__snake_case ,     __snake_case  )
              __magic_name__      				=    0
              __magic_name__      				=    neighborhood[index_of_best_solution]
              __magic_name__      				=    len(__snake_case  ) - 1

              __magic_name__      				=    False
              while not found:
                     __magic_name__      				=    0
                     while i < len(__snake_case  ):
                            if best_solution[i] != solution[i]:
                                   __magic_name__      				=    best_solution[i]
                                   __magic_name__      				=    solution[i]
                                   break
                            __magic_name__      				=    i + 1

                     if [first_exchange_node, second_exchange_node] not in tabu_list and [
                         second_exchange_node,
                         first_exchange_node,
                     ] not in tabu_list:
                            tabu_list.append([first_exchange_node, second_exchange_node]  )
                            __magic_name__      				=    True
                            __magic_name__      				=    best_solution[:-1]
                            __magic_name__      				=    neighborhood[index_of_best_solution][best_cost_index]
                            if cost < best_cost:
                                   __magic_name__      				=    cost
                                   __magic_name__      				=    solution
                     else:
                            __magic_name__      				=    index_of_best_solution + 1
                            __magic_name__      				=    neighborhood[index_of_best_solution]

              if len(__snake_case  ) >= size:
                     tabu_list.pop(0  )

              __magic_name__      				=    count + 1

       return best_solution_ever, best_cost


def 							A						(				__snake_case: Union[str, Any]=None  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    generate_neighbours(args.File  )

       __magic_name__				,  __magic_name__      				=    generate_first_solution(
           args.File ,     __snake_case  )

       __magic_name__				,  __magic_name__      				=    tabu_search(
           __snake_case ,     __snake_case ,     __snake_case ,     args.Iterations ,     args.Size ,     )

       print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""  )


if __name__ == "__main__":
 snake_case : str     		=		argparse.ArgumentParser(description="""Tabu Search""")
 parser.add_argument(
     """-f""",
     """--File""",
     type=str,
     help="""Path to the file containing the data""",
     required=True,
 )
 parser.add_argument(
     """-i""",
     """--Iterations""",
     type=int,
     help="""How many iterations the algorithm should perform""",
     required=True,
 )
 parser.add_argument(
     """-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
 )

 # Pass the arguments to main method
 main(parser.parse_args())